Collecting notes for LLFS proposal
Automatic Gating of flow cytometry data
Automated gating was able to match the performance of central manual analysis for all tested panels, exhibiting little to no bias and comparable variability. Standardized staining, data collection, and automated gating can increase power, reduce variability, and streamline analysis for immunophenotyping.
The two top performing gating algorithms - OpenCyto (v. 1.7.4), flowDensity (v. 1.4.0) - in a study run by the FlowCAP consortium aimed at selecting the best performing algorithms for this larger study were chosen for the analysis presented in this paper.
Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium Finak, Langweiler, Jaimes, et al. (2016)
OpenCyto Finak, Frelinger, Jiang, et al. (2014)
We propose to use OpenCyto to perform systematic and reproducable gating of 28 immune cell subsets. Gating is standardized via a .csv file describing the algorithmic approach for each step of the gating hierarchy.
We evaluated the performance of OpenCyto using internal data for 151 manually gated (Jflow software) samples across 15 gates. The correlation between manual and OpenCyto gating was high (rho=0.9846 ,p-value <2e-16)
- Mimics manual gating by focusing on 2 channels at a time
- can follow traditional gate hierarchy
- e.g. gate lymph, then single, then live, etc
- Pipeline templates defined in .csv file
- defines algorithmic approach for each gate to be applied across many samples
- not “just push go”, takes some setup for a decent template
- Results are interpretable and labelled populations
- not geared toward detecting novel cell types
Novel subsets
t-SNE is a visualization method, and not sure if it can be directly used for novel subset detection